This document describes the development of an interactive visualizer for pathfinding algorithms using React. It discusses choosing React for its component-based architecture and efficient rendering. The visualizer will break down into reusable components like Graph and Node. React's virtual DOM will enable smooth updating as the algorithm progresses. The document outlines implementing algorithms like Dijkstra's and A*, tracking progress through React state, and handling user interactions to update the visualization in real-time. The visualizer aims to provide an engaging experience for users to explore pathfinding algorithms through an interactive interface.

1. VISUAL ANALYSIS
OF PATHFINDER
ALGORITHMS

2. 01 INTRODUCTION
TABLE OF CONTENTS
03
IMPLEMENTATION
04
METHODOLOGY
05
TECHNICAL DISCUSSION
07 CONCLUSION
06 DEMONSTRATION
02 PROBLEM STATEMENT

3. INTRODUCTION
01
• This project allows users to understand and explore the inner workings of
various pathfinding algorithms in a visual and interactive manner. It provides a
dynamic representation of the algorithms, enabling users to observe how
different techniques navigate through a graph or network to find the optimal
path.
• The visualizer allows users to experiment with different scenarios and
parameters. They can add the constraints or Randomize it with the shortest
path and observe how these modifications affect the algorithm's behavior and
the resulting path. This interactive aspect promotes learning and empowers
users to gain insights into algorithm performance and optimization.
• It promotes understanding, experimentation, and optimization, making it an
invaluable tool for both learning and practical applications in fields where
efficient pathfinding is crucial.

4. PROBLEM STATEMENT
02
• The problem addressed by the project is the difficulty in comprehending the
inner workings of pathfinding algorithms without visual representation.
Textual explanations alone may not effectively convey the complexities of the
pathfinding process, making it challenging for users to evaluate the
algorithms’ efficiency and effectiveness.
• The lack of a user-friendly platform that offers interactive visualizations
hinders users’ ability to grasp the performance of different algorithms in
diverse scenarios.

5. METHODOLOGY
03
• UI libraries that provide interactive
graphics capabilities suitable for your
pathfinding algorithm visualizer. Some
popular choices include ReactJS with
libraries
• libraries that offer features like drawing
nodes and edges, handling user
interactions, and updating the
visualization in real-time.
• Implementation of the selected
pathfinding algorithms, such as Dijkstra's
algorithm, A* algorithm, or Breadth-first
search (BFS), according to the chosen UI
library's conventions.
• Ensure the algorithms correctly navigate
the graph, update the visualization in
each step, and track visited nodes and
the final path.
CHOOSING FRAMEWORK
GRAPH DATA STRUCTURE
IMPLEMENTATION

6. TECHNICAL DISCUSION
03
• Depth-First Search (DFS) is a graph traversal
algorithm that explores deeply along each branch
before backtracking. It starts from a given node,
explores as far as possible, and backtracks to visit
other unvisited nodes.
• DFS can be implemented using a stack or
recursion and is commonly used for traversing or
searching connected components in a graph.
• While DFS may not guarantee the shortest path in
pathfinding, it is useful for scenarios like maze-
solving or exhaustive graph exploration.
• Breadth-First Search (BFS) is a widely used
graph traversal algorithm that explores
vertices in breadth-first order.
• It starts from a given node and visits all its
neighboring nodes before moving on to their
neighbors, and so on.
• BFS can be implemented using a queue or
a deque and is commonly used to find the
shortest path in an unweighted graph.
• In pathfinding, BFS guarantees to find the
shortest path in terms of the number of
edges, making it suitable for scenarios
where equal-cost paths are desired or when
weights are not present.

7. • Dijkstra's Algorithm is an efficient algorithm for
finding the shortest path between nodes in a
weighted graph.
• It starts from a source node and expands the
frontier by visiting neighboring nodes with the
lowest cumulative cost.
• By using a priority queue or min-heap, it
efficiently selects the node with the minimum cost
at each step. Dijkstra's Algorithm maintains a
distance table to track the shortest distances and a
previous node table to reconstruct the shortest
path.
• It finds application in navigation systems, routing
protocols, and network optimization.
• The A* (A-star) algorithm is an informed
search algorithm used for pathfinding. It
combines BFS and Dijkstra's Algorithm by
considering both the cost to reach a node
and an estimated cost to the goal.
• A* uses a heuristic function like Manhattan or
Euclidean distance to estimate remaining
costs.
• It maintains a priority queue based on
combined costs and heuristic values to guide
the search.
• A* is widely used in GPS navigation, video
games, and robot motion planning.

8. React is a JavaScript library for building user interfaces, known for its component-based
architecture and efficient rendering. When creating a path visualizer using React, we will
utilize its core features to develop a dynamic and interactive user interface.
React allows to break down of the visualizer into reusable components like Graph, Node,
Edge, Controls, and Visualization Area, enabling modular development and code reusability.
With React's virtual DOM and efficient diffing algorithm, we can update only the necessary
components, ensuring the smooth and efficient rendering of the visualization as the
pathfinding algorithm progresses.
React's state management capabilities facilitate the tracking of algorithm progress, enabling
real-time updates to the visualization. By leveraging React's strengths, We can create a
powerful and visually appealing path visualizer that provides an engaging and intuitive
experience for users to explore and understand pathfinding algorithms.
React

9. IMPLEMENTATION
05
• Creating a data structure to represent the
graph. This could involve storing node
coordinates, adjacency lists or matrices, and
weights/costs for edges.
• Implementation of the pathfinding
algorithms to visualize, such as Dijkstra's
algorithm, A* algorithm, or BFS, in separate
algorithm files.
• Writing the functions to execute the
chosen algorithms and updating the graph
data structure and visualization accordingly.
• Update the graph data structure and trigger the
appropriate algorithm functions based on user
input.
• Use CSS or inline styles to animate or highlight
the algorithm's execution, such as marking visited
nodes or animating the pathfinding process.
• Update the Graph and Node components to
visually represent the graph, including nodes,
edges, and any obstacles.
• Use React's lifecycle methods or useEffect hook
to update the visualization in real-time as the
algorithm progresses.

10. • Implementation of event handlers for
user interactions, such as randomizing
selecting start and end points with
adding random obstacles, or adjusting
visualization settings.
• Update the visualization and trigger the
appropriate algorithms or graph
operations based on user input.
• Utilize the capabilities of the UI library to
update the visualization in real time. Use
animations or transitions to show the
algorithm's progress and visualize the
pathfinding process step by step.
USER HANDLE INTERACTION USER INTERFACE

11. DEMONSTRATION
06

12. CONCLUSION
07
• In conclusion, pathfinding algorithm visualizers created using React offer an
interactive way to understand and explore algorithms like DFS, BFS, Dijkstra's
Algorithm, and A*. These visualizers leverage React's capabilities to provide a
seamless and efficient platform for building engaging visualizations.
• Users can gain insights into the algorithms' behavior and applications,
enhancing their problem-solving skills and fostering a passion for algorithmic
exploration.
• The combination of React and pathfinding algorithm visualizers not only
fosters a deeper understanding of these algorithms but also enhances
problem-solving skills and fosters a passion for algorithmic exploration.

13. THANK YOU